We implement our method with PyTorch on a GEFORCE RTX 3090 GPU.
- Linux
- Python 3
- CPU or NVIDIA GPU + CUDA CuDNN
- UIEB: https://li-chongyi.github.io/proj_benchmark.html
- EUVP: http://irvlab.cs.umn.edu/resources/euvp-dataset/
- MIT-Adobe 5K: https://data.csail.mit.edu/graphics/fivek/
- Experiments on UIEB dataset
# Train
CUDA_VISIBLE_DEVICES=0 python train.py --model Ideal --name Ideal_uieb --dataset_root uieb_dataset_url --dataset_mode uieb --batch_size 8
# Test
CUDA_VISIBLE_DEVICES=0 python test.py --model Ideal --name Ideal_uieb --dataset_root uieb_dataset_url --dataset_mode uieb --batch_size 1
# Metrics: MSE, PSNR, SSIM
CUDA_VISIBLE_DEVICES=0 python metrics/evaluations-enhancement.py --results_root results/Ideal_uieb/test_latest/images/ --dataname uieb- Experiments on EUVP dataset
# Train
CUDA_VISIBLE_DEVICES=0 python train.py --model Ideal --name Ideal_euvp --dataset_root euvp_paired_dataset_url --dataset_mode euvp --batch_size 8
# Test
CUDA_VISIBLE_DEVICES=0 python test.py --model Ideal --name Ideal_euvp --dataset_root euvp_paired_dataset_url --dataset_mode euvp --batch_size 1
# Metrics: MSE, PSNR, SSIM
CUDA_VISIBLE_DEVICES=0 python metrics/evaluations-enhancement.py --results_root results/Ideal_euvp/test_latest/images/ --dataname euvp# Train
CUDA_VISIBLE_DEVICES=0 python train.py --model NonIdealGAN --name NonIdealGAN_euvp --dataset_root euvp_unpaired_dataset_url --unaligned_dataset euvp --batch_size 8
# Test
CUDA_VISIBLE_DEVICES=0 python test.py --model NonIdealGAN --name NonIdealGAN_euvp --dataset_root euvp_paired_dataset_url --dataset_mode euvp --batch_size 1
# Metrics: MSE, PSNR, SSIM
CUDA_VISIBLE_DEVICES=0 python metrics/evaluations-enhancement.py --results_root results/NonIdealGAN_euvp/test_latest/images/ --dataname euvp
# Metrics: UIQM
python metrics/uiqm_metrics.py --results_root results/NonIdealGAN_euvp/test_latest/images/# Train
CUDA_VISIBLE_DEVICES=0 python train.py --model NonIdealRAS --name NonIdealRAS_In-air --dataset_root euvp_unpaired_dataset_url --unaligned_dataset adobe5k --batch_size 8
# Test
CUDA_VISIBLE_DEVICES=0 python test.py --model NonIdealRAS --name NonIdealRAS_In-air --dataset_root euvp_paired_dataset_url --dataset_mode euvp --batch_size 1
# Metrics: UIQM
python metrics/uiqm_metrics.py --results_root results/NonIdealRAS_In-air/test_latest/images/- Download pre-trained models from Google Drive or BaiduCloud (access code: 70i0), and put
pre-trained/*in the directorycheckpoints/. Run test command to obtain the results.
For some of the data modules and model functions used in this source code, we need to acknowledge the repositories of CycleGAN and IntrinsicHarmony.
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